Engineering a HIPAA-gated Sentiment-Aware Agentic Mesh to transform generic post-discharge follow-ups into high-conversion clinical interventions that reduce readmission risk.
Trusted by Leading Fortune 500 Innovators
High-volume surgical centers requiring automated follow-up care coordination without increasing nurse burnout.
AI Architect + NLP Engineers + HIPAA Compliance Specialist embedded within the Clinical Innovation unit.
Closing the 'Post-Op Information Gap' to ensure patients follow care plans and attend critical follow-up appointments.
FHIR-integrated Kafka pipelines, Agentic NLP engine, and HIPAA-secure messaging gateways.
The client’s post-discharge strategy relied on automated 'robotic' SMS pings and manual nurse calls. Patients suffering from post-op confusion or anxiety ignored these static prompts, leading to a 40% follow-up abandonment rate and preventable readmissions.
The risk was clinical: the 'Reality Gap' between discharge instructions and home-recovery execution was widening. Generic messaging failed to detect patients in rising distress, while clinical staff were too saturated to perform manual triage for every discharge.
One-way SMS pings sent to all patients regardless of recovery status or surgery type.
Conversational AI detects distress signals and adjusts intervention urgency in real-time.
Follow-up systems were disconnected from live EHR discharge and readmission data.
Bidirectional sync ensures follow-up loops are informed by live clinical records.
Clinical triage capacity limited by total nurse headcount and manual call-handling time.
Handles 100% of patient follow-ups autonomously, escalating only the top 10% of high-risk cases.
Autonomous agents identified patients expressing 'uncontrolled pain' or 'medication confusion' with 94% accuracy vs clinical gold standard.
Engineered a secure data mesh that performs inference in a zero-persistence environment, ensuring PHI never leaves the clinical cloud perimeter.
Automated outreach sequences triggered precisely 6, 24, and 72 hours post-op based on live surgical metadata.
Pre-built models for medical-intent recognition and surgical-recovery sentiment analysis.
Production-ready templates for secure patient messaging and PII-redacted logging.
Real-time telemetry dashboards monitoring readmission trends and patient engagement scores.
Automated flagging of high-readmission risk patients based on engagement patterns.
Empathetic agentic loops successfully re-engaged patients who traditionally dropped out of the care funnel.
Autonomous triage reduced the nurse call queue by auto-handling stable patient queries.
Detecting early distress through sentiment analysis allowed for preventive interventions before an ER visit was required.
Client Testimonial
Coretus didn't just build a chatbot—they engineered a sentiment-aware clinical bridge. For the first time, we can detect patient distress at scale and intervene before a readmission occurs. Adherence is up 30%, and our nursing staff finally has the triage support they need.
Chief Medical Information Officer